U.S. patent number 11,064,894 [Application Number 16/106,189] was granted by the patent office on 2021-07-20 for method and device to manage fluid volumes in the body.
This patent grant is currently assigned to Medtronic, Inc.. The grantee listed for this patent is Medtronic, Inc.. Invention is credited to Martin T. Gerber, Christopher M. Hobot, VenKatesh R. Manda, Orhan Soykan.
United States Patent |
11,064,894 |
Soykan , et al. |
July 20, 2021 |
Method and device to manage fluid volumes in the body
Abstract
A system and method for determining the amount of fluid to be
removed from a dialysis patient is disclosed. The system utilizes
sensors and a computer. The computer obtains the input parameters
from the sensors, along with information added directly by the
user, and performs a forward algorithm to determine a recommended
change in patient fluid level. As fluid is removed, the effect of
the removal on the parameters is detected by the sensors and
re-transmitted back to the computer. The computer then performs a
backward algorithm to refine the variables used in the forward
algorithm and obtain more accurate results. The system and method
provide for changing the amount of fluid removed from the patient
based on the results of the algorithm and the data received from
the sensors.
Inventors: |
Soykan; Orhan (Shoreview,
MN), Hobot; Christopher M. (Rogers, MN), Gerber; Martin
T. (Maple Grove, MN), Manda; VenKatesh R. (Stillwater,
MN) |
Applicant: |
Name |
City |
State |
Country |
Type |
Medtronic, Inc. |
Minneapolis |
MN |
US |
|
|
Assignee: |
Medtronic, Inc. (Minneapolis,
MN)
|
Family
ID: |
53005427 |
Appl.
No.: |
16/106,189 |
Filed: |
August 21, 2018 |
Prior Publication Data
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Document
Identifier |
Publication Date |
|
US 20180353136 A1 |
Dec 13, 2018 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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14539437 |
Nov 12, 2014 |
10076283 |
|
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61899875 |
Nov 4, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61M
1/1603 (20140204); G16H 50/20 (20180101); A61B
5/02405 (20130101); A61B 5/4848 (20130101); A61B
5/053 (20130101); A61B 5/7264 (20130101); A61B
5/021 (20130101); A61B 5/4875 (20130101); G16Z
99/00 (20190201); A61B 5/08 (20130101); A61B
5/14552 (20130101); G16H 20/40 (20180101); G16H
10/60 (20180101); A61B 5/7246 (20130101); A61M
1/3403 (20140204); A61B 5/361 (20210101); G16H
40/63 (20180101); G16H 50/50 (20180101); A61B
5/0205 (20130101); A61M 1/282 (20140204); A61B
5/201 (20130101); A61B 5/4806 (20130101); A61M
2205/3523 (20130101) |
Current International
Class: |
A61M
37/00 (20060101); G16H 50/50 (20180101); A61B
5/024 (20060101); G16H 20/40 (20180101); A61B
5/08 (20060101); A61B 5/053 (20210101); A61M
1/28 (20060101); A61M 1/16 (20060101); A61B
5/0205 (20060101); G16H 50/20 (20180101); A61B
5/1455 (20060101); A61B 5/00 (20060101); A61B
5/361 (20210101); A61B 5/20 (20060101); G16H
40/63 (20180101); A61M 1/34 (20060101); G16H
10/60 (20180101); A61B 5/021 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
|
|
|
|
|
|
|
101193667 |
|
Jun 2008 |
|
CN |
|
103037917 |
|
Apr 2013 |
|
CN |
|
3224823 |
|
Jan 1984 |
|
DE |
|
266795 |
|
Nov 1987 |
|
EP |
|
0272414 |
|
Oct 1991 |
|
EP |
|
0330892 |
|
Jul 1994 |
|
EP |
|
1124599 |
|
May 2000 |
|
EP |
|
1175238 |
|
Nov 2000 |
|
EP |
|
1281351 |
|
Feb 2003 |
|
EP |
|
2308526 |
|
Oct 2003 |
|
EP |
|
1364666 |
|
Nov 2003 |
|
EP |
|
1523347 |
|
Jan 2004 |
|
EP |
|
1523350 |
|
Jan 2004 |
|
EP |
|
0906768 |
|
Feb 2004 |
|
EP |
|
1691863 |
|
Apr 2005 |
|
EP |
|
2116269 |
|
Feb 2008 |
|
EP |
|
1450879 |
|
Oct 2008 |
|
EP |
|
1514562 |
|
Apr 2009 |
|
EP |
|
2219703 |
|
May 2009 |
|
EP |
|
1592494 |
|
Jun 2009 |
|
EP |
|
2100553 |
|
Sep 2009 |
|
EP |
|
2398529 |
|
Nov 2010 |
|
EP |
|
2575827 |
|
Dec 2010 |
|
EP |
|
2100553 |
|
Aug 2011 |
|
EP |
|
2576453 |
|
Dec 2011 |
|
EP |
|
2701580 |
|
Nov 2012 |
|
EP |
|
2701595 |
|
Nov 2012 |
|
EP |
|
1345856 |
|
Mar 2013 |
|
EP |
|
2344220 |
|
Apr 2013 |
|
EP |
|
1351756 |
|
Jul 2013 |
|
EP |
|
2190498 |
|
Jul 2013 |
|
EP |
|
2701596 |
|
Mar 2014 |
|
EP |
|
1582226 |
|
Jan 2016 |
|
EP |
|
S551980138462 |
|
Oct 1980 |
|
JP |
|
S63-143077 |
|
Nov 1987 |
|
JP |
|
2002533170 |
|
Oct 2002 |
|
JP |
|
2002542900 |
|
Dec 2002 |
|
JP |
|
2003235965 |
|
Aug 2003 |
|
JP |
|
2005-533573 |
|
Nov 2005 |
|
JP |
|
5099464 |
|
Oct 2012 |
|
JP |
|
1995003839 |
|
Feb 1995 |
|
WO |
|
WO 1998054563 |
|
Dec 1998 |
|
WO |
|
9937342 |
|
Jul 1999 |
|
WO |
|
0057935 |
|
Oct 2000 |
|
WO |
|
WO2000057935 |
|
Oct 2000 |
|
WO |
|
200066197 |
|
Nov 2000 |
|
WO |
|
2000066197 |
|
Nov 2000 |
|
WO |
|
200170307 |
|
Sep 2001 |
|
WO |
|
2001085295 |
|
Sep 2001 |
|
WO |
|
0185295 |
|
Nov 2001 |
|
WO |
|
1085295 |
|
Nov 2001 |
|
WO |
|
2002013691 |
|
Feb 2002 |
|
WO |
|
2003043677 |
|
May 2003 |
|
WO |
|
2003043680 |
|
May 2003 |
|
WO |
|
2003051422 |
|
Jun 2003 |
|
WO |
|
2004008826 |
|
Jan 2004 |
|
WO |
|
2004009156 |
|
Jan 2004 |
|
WO |
|
2004009158 |
|
Jan 2004 |
|
WO |
|
2004030716 |
|
Apr 2004 |
|
WO |
|
2004030717 |
|
Apr 2004 |
|
WO |
|
2004064616 |
|
Aug 2004 |
|
WO |
|
2005033701 |
|
Apr 2005 |
|
WO |
|
2005061026 |
|
Jul 2005 |
|
WO |
|
2005123230 |
|
Dec 2005 |
|
WO |
|
2006011009 |
|
Feb 2006 |
|
WO |
|
2006017446 |
|
Feb 2006 |
|
WO |
|
2007038347 |
|
Apr 2007 |
|
WO |
|
2007089855 |
|
Aug 2007 |
|
WO |
|
2008037410 |
|
Apr 2008 |
|
WO |
|
2009026603 |
|
Dec 2008 |
|
WO |
|
2009024566 |
|
Feb 2009 |
|
WO |
|
2009026603 |
|
Mar 2009 |
|
WO |
|
2009061608 |
|
May 2009 |
|
WO |
|
2009094184 |
|
Jul 2009 |
|
WO |
|
2009157877 |
|
Dec 2009 |
|
WO |
|
2009157878 |
|
Dec 2009 |
|
WO |
|
2010024963 |
|
Mar 2010 |
|
WO |
|
2010028860 |
|
Mar 2010 |
|
WO |
|
2010028860 |
|
Mar 2010 |
|
WO |
|
2010033314 |
|
Mar 2010 |
|
WO |
|
2010033699 |
|
Mar 2010 |
|
WO |
|
2010077851 |
|
Jul 2010 |
|
WO |
|
2010096659 |
|
Oct 2010 |
|
WO |
|
2010121820 |
|
Oct 2010 |
|
WO |
|
2011025705 |
|
Mar 2011 |
|
WO |
|
2011026645 |
|
Mar 2011 |
|
WO |
|
2011137693 |
|
Nov 2011 |
|
WO |
|
WO2011161056 |
|
Dec 2011 |
|
WO |
|
2012042323 |
|
Apr 2012 |
|
WO |
|
2012050781 |
|
Apr 2012 |
|
WO |
|
2012051996 |
|
Apr 2012 |
|
WO |
|
2012073420 |
|
Jul 2012 |
|
WO |
|
2012148781 |
|
Nov 2012 |
|
WO |
|
2012148786 |
|
Nov 2012 |
|
WO |
|
2012148787 |
|
Nov 2012 |
|
WO |
|
2012148789 |
|
Nov 2012 |
|
WO |
|
2012162515 |
|
Nov 2012 |
|
WO |
|
20120277551 |
|
Nov 2012 |
|
WO |
|
WO2012148788 |
|
Nov 2012 |
|
WO |
|
WO 20120148784 |
|
Nov 2012 |
|
WO |
|
2012148784 |
|
Dec 2012 |
|
WO |
|
2012172398 |
|
Dec 2012 |
|
WO |
|
2013019179 |
|
Feb 2013 |
|
WO |
|
2013019994 |
|
Feb 2013 |
|
WO |
|
2013025844 |
|
Feb 2013 |
|
WO |
|
2013028809 |
|
Feb 2013 |
|
WO |
|
2013101292 |
|
Jul 2013 |
|
WO |
|
2013103607 |
|
Jul 2013 |
|
WO |
|
2013103906 |
|
Jul 2013 |
|
WO |
|
2013110906 |
|
Aug 2013 |
|
WO |
|
2013110919 |
|
Aug 2013 |
|
WO |
|
2013114063 |
|
Aug 2013 |
|
WO |
|
2013121162 |
|
Aug 2013 |
|
WO |
|
2013140346 |
|
Sep 2013 |
|
WO |
|
2013141896 |
|
Sep 2013 |
|
WO |
|
2013101292 |
|
Oct 2013 |
|
WO |
|
14066254 |
|
May 2014 |
|
WO |
|
14066255 |
|
May 2014 |
|
WO |
|
14077082 |
|
May 2014 |
|
WO |
|
2014121162 |
|
Aug 2014 |
|
WO |
|
2014121163 |
|
Aug 2014 |
|
WO |
|
2014121167 |
|
Aug 2014 |
|
WO |
|
2014121169 |
|
Aug 2014 |
|
WO |
|
WO 20140121161 |
|
Aug 2014 |
|
WO |
|
WO2015081221 |
|
Jun 2015 |
|
WO |
|
WO 20150159280 |
|
Oct 2015 |
|
WO |
|
Other References
[NPL376] Gambro AK 96 Dialysis Machine Operators Manual, Dec. 2012.
p. 1-140. cited by applicant .
[NPL376] Gambro AK 96 Dialysis Machine Operators Manual, Dec. 2012.
p. 141-280. cited by applicant .
[NPL376] Gambro AK 96 Dialysis Machine Operators Manual, Dec. 2012.
p. 281-420. cited by applicant .
[NPL376] Gambro AK 96 Dialysis Machine Operators Manual, Dec. 2012.
p. 421-534. cited by applicant .
[NPL325] Albert, Fluid Management Strategies in Heart Failure,
Critical Care Nurse, 32:20-32, 2012. cited by applicant .
[NPL326] PCT/US2014/065201 International Search Report dated May
26, 2015. cited by applicant .
[NPL328] Genovesi, et al., Nephrology, Dialysis, Transplantation
2009; 24(8):2529-2536. cited by applicant .
[NPL32] Secemsky, et. al, High prevalence of cardiac autonomic
dysfunction and T-wave alternans in dialysis patients. Heart
Rhythm, Apr. 2011, 592-598 : vol. 8, No. 4. cited by applicant
.
[NPL339] U.S. Appl. 13/424,517 IDS, filed Aug. 2, 2012. cited by
applicant .
[NPL340] U.S. Appl. 13/424,517, IDS filed Dec. 2, 2013. cited by
applicant .
[NPL35] Wei, et. al., Fullerene-cryptand coated piezoelectric
crystal urea sensor based on urease, Analytica Chimica Acta,
2001,77-85:437. cited by applicant .
[NPL37] US NonProvisional Application, U.S. Appl. No. 13/368,225
dated Feb. 7, 2012. cited by applicant .
[NPL383] Leifer et al., A Study on the Temperature Variation of
Rise Velocity for Large Clean Bubbles, J. Atmospheric & Oceanic
Tech., vol. 17, pp. 1392-1402, Oct. 2000. cited by applicant .
[NPL384] Talaia, Terminal Velocity of a Bubble Rise in a Liquid
Column, World Acad. of Sci., Engineering & Tech., vol. 28, pp.
264-268, Published Jan. 1, 2007. cited by applicant .
[NPL386] The FHN Trial Group. In-Center. Hemodialysis Six Times per
Week versus Three Times per Week, New England Journal of Medicine,
2010 Abstract. cited by applicant .
[NPL39] PCT/US2012/034332, International Search Report, dated Jul.
5, 2012. cited by applicant .
[NPL46] Siegenthaler, et al., Pulmonary fluid status monitoring
with intrathoracic impedance, Journal of Clinical Monitoring and
Computing, 24:449-451, published Jan. 12, 2011. cited by applicant
.
[NPL477] Office Action in U.S. Appl. No. 13/757,792 dated Apr. 6,
2015. cited by applicant .
[NPL47] U.S. Appl. No. 61/480,544. cited by applicant .
[NPL483] Office Action in U.S. Appl. No. 13/424,525 dated Aug. 11,
2015. cited by applicant .
[NPL486] Office Action in U.S. Appl. No. 13/424,525 dated Oct. 20,
2016. cited by applicant .
[NPL494] John Wm Agar: Review: Understanding sorbent dialysis
systems, Nephrology, vol. 15, No. 4, Jun. 1, 2010, pp. 406-411.
cited by applicant .
[NPL495] European Office Action in Application 12717020.7 dated
Sep. 14, 2016. cited by applicant .
[NPL500] Office Action in U.S. Appl. No. 14/554,272 dated Aug. 8,
2016. cited by applicant .
[NPL501] Office Action in U.S. Appl. No. 13/424,467 dated Oct. 16,
2013. cited by applicant .
[NPL502] Office Action in U.S. Appl. No. 13/424,467 dated Mar. 3,
2014. cited by applicant .
[NPL503] Office Action in U.S. Appl. No. 13/424,490 dated Oct. 22,
2013. cited by applicant .
[NPL504] Office Action in U.S. Appl. No. 13/424,490 dated Mar. 10,
2014. cited by applicant .
[NPL505] Office Action in U.S. Appl. No. 13/424,490 dated Jul. 14,
2014. cited by applicant .
[NPL506] Office Action in U.S. Appl. No. 13/424,490 dated Dec. 5,
2014. cited by applicant .
[NPL507] Office Action in U.S. Appl. No. 13/424,525 dated Sep. 29,
2014. cited by applicant .
[NPL508] Office Action in U.S. Appl. No. 13/424,525 dated May 6,
2015. cited by applicant .
[NPL509] Office Action in U.S. Appl. No. 13/424,454 dated Oct. 17,
2013. cited by applicant .
[NPL510] Office Action in U.S. Appl. No. 13/424,454 dated Mar. 10,
2014. cited by applicant .
[NPL511] Office action in U.S. Appl. 13/424,429 dated Oct. 15,
2015. cited by applicant .
[NPL512] Office Action in U.S. Appl. 12/571,127 dated Feb. 27,
2014. cited by applicant .
[NPL513] Office Action in U.S. Appl. 12/571,127 dated Jul. 6, 2015.
cited by applicant .
[NPL514] Office Action in U.S. Appl. 12/571,127 dated Dec. 17,
2015. cited by applicant .
[NPL521] Office Action in U.S. Appl. No. 14/554,338 dated Jun. 7,
2016. cited by applicant .
[NPL522] Office Action in U.S. Appl. No. 14/554,338 dated Sep. 28,
2016. cited by applicant .
[NPL524] Office Action in U.S. Appl. No. 13/424,429 dated Oct. 15,
2015. cited by applicant .
[NPL525] Office Action in U.S. Appl. No. 12/571,127 dated Feb. 27,
2014. cited by applicant .
[NPL526] Office Action in U.S. Appl. No. 12/571,127 dated Jul. 6,
2015. cited by applicant .
[NPL105] Brynda, et. al., The detection of toman 2-microglcbuiin by
grating coupler immunosensor with three dimensional antibody
networks. Biosensors & Bioelectronics, 1999, 363-368, 14(4).
cited by applicant .
[NPL10] Wheaton, et al., Dowex Ion Exchange Resins--Fundamentals of
Ion Exchange; Jun. 2000, pp. 1-9.
http://www.dow.com/scripts/litorder.asp?filepath=liguidseps/pdfs/noreg/17-
7-01837.pdf. cited by applicant .
[NPL111] Zhong, et. al., Miniature urea sensor based on H(+)-ion
sensitive field effect transistor and its application in clinical
analysis, Chin. J. Biotechnol., 1992, 57-65. 8(1). cited by
applicant .
[NPL119] PCT/US2012/034331, International Search Report and Written
Opinion dated Jul. 9, 2012. cited by applicant .
[NPL121] Roberts M, The regenerative dialysis (REDY) sorbent
system. Nephrology, 1998, 275-278:4. cited by applicant .
[NPL138] U.S. Appl. No. 61/480,544. cited by applicant .
[NPL139] U.S. Appl. No. 61/480,541 dated Apr. 29, 2011. cited by
applicant .
[NPL142] Hemametrics, Crit-Line Hematocrit Accuracy, 2003, 1-5,
vol. 1, Tech Note No. 11 (Rev. D). cited by applicant .
[NPL144] Weissman, S., et al., Hydroxyurea-induced hepatitis in
human immunodeficiency virus-positive patients. Clin. Infec. Dis,
(Jul. 29, 1999): 223-224. cited by applicant .
[NPL146] PCT/US2012/034334, International Search Report, dated Jul.
6, 2012. cited by applicant .
[NPL147] PCT/US2012/034335, International Search Report, dated Sep.
5, 2012. cited by applicant .
[NPL148] PCT/US/2012/034327, International Search Report, dated
Aug. 13, 2013. cited by applicant .
[NPL149] PCT/US/2012/034329, International Search Report, dated
Dec. 3, 2012. cited by applicant .
[NPL14] Foley, et al., Long Interdialytic Interval and Martality
among Patients Receiving Hemodialysis, N Engl Jrnl Med.
2011:365(12):1099-1107. cited by applicant .
[NPL15] PCT International Search Report from International
Application No. PCT/US2014/067650, dated Nov. 27, 2013. cited by
applicant .
[NPL169] Wang, Fundamentals of intrathoracic impedance monitoring
in heart failure, Am. J. Cardiology, 2007, 3G-10G: Suppl. cited by
applicant .
[NPL16] PCT/US2014/067650 International Search Report Written
Opinion dated Mar. 9, 2015. cited by applicant .
[NPL170] Bleyer, et al, Kidney International. Jun. 2006;
69(12):2268-2273. cited by applicant .
[NPL176] Bleyer, et. al., Sudden and cardiac death rated in
hemodialysis patients, Kidney International. 1999, 1553-1559: 55.
cited by applicant .
[NPL180] PCT/US2012/034335, International Preliminary Report on
Patentability, dated Nov. 7, 2013. cited by applicant .
[NPL181] PCT/US2012/034303, Internationa Search Report, dated Jul.
6, 2013. cited by applicant .
[NPL186] PCT/US2012/034332, Internatonal Preliminary Report on
Patentability, dated Oct. 29, 2013. cited by applicant .
[NPL187] PCT/US2012/034333, International Preliminary Report on
Patentability, dated Oct. 29, 2013. cited by applicant .
[NPL188] PCT/US2012/034333, International Search Report, dated Aug.
29, 2012. cited by applicant .
[NPL188] PCT/US2012/034333, International Search Report, dated Aug.
29, 2013. cited by applicant .
European Office Action for App. No. 14859115.9, dated Mar. 25,
2020. cited by applicant .
[NPL195] PCT/US2012/034327, International Preliminary Report on
Patentability, dated Oct. 29, 2013. cited by applicant .
[NPL197] PCT/US2012/034330, International Preliminary Report on
Patentability, dated Oct. 29, 2013. cited by applicant .
[NPL205] Culleton, BF et al. Effect of Frequent Nocturnal
Hemodialysis vs. Conventional Hemodialysis on Left Ventricular Mass
and Quality of Life. 2007 Journal of the American Medical
Association 298 (11), 1291-1299. cited by applicant .
[NPL217] U.S. Appl. No. 13/757,722, filed Feb. 1, 2013. cited by
applicant .
[NPL218] U.S. Appl. No. 13/757,794, filed Feb. 2, 2012. cited by
applicant .
[NPL219] U.S. Appl. No. 13/791,755, filed Mar. 8, 2013. cited by
applicant .
[NPL21] U.S. Appl. No. 13/424,479 dated Nov. 1, 2012. cited by
applicant .
[NPL220] U.S. Appl. No. 13/757,792, filed Feb. 2, 2013. cited by
applicant .
[NPL222] U.S. Appl. No. 13/757,794, filed Feb. 2, 2013. cited by
applicant .
[NPL227] U.S. Appl. No. 13/837,287, filed Mar. 15, 2013. cited by
applicant .
[NPL22] U.S. Appl. No. 13/424,429 dated Nov. 1, 2012. cited by
applicant .
[NPL230] Redfield, et. al, Restoration of renal response to atria!
natriuretic factor in experimental low-output heat failure, Am. J.
Physiol., Oct. 1, 1989, R917-923:257. cited by applicant .
[NPL231] Rogoza, et. al., Validation of A&D UA-767 device for
the self-measurement of blood pressure, Blood Pressure Monitoring,
2000, 227-231, 5(4). cited by applicant .
[NPL233] PCT/US2012/034329, International Preliminary Report on
Patentability, dated Oct. 29, 2013. cited by applicant .
[NPL234] Lima, et. al., An electrochemical sensor based on
nanostructure hollsndite-type manganese oxide for detection of
potassium ion, Sensors, Aug. 24, 2009, 6613-8625, 9. cited by
applicant .
[NPL235] MacLean, et, al., Effects of hindlimb contraction on
pressor and muscle interstitial metabolite responses in the cat, J.
App. Physiol., 1998, 1583-1592, 85(4). cited by applicant .
[NPL237] U.S. Appl. No. 13/757,693, dated Feb. 1, 2013. cited by
applicant .
[NPL238] PCT Application, PCT/US20013/020404, filed Jan. 4, 2013.
cited by applicant .
[NPL23] U.S. Appl. No. 13/424,525. cited by applicant .
[NPL240] US Application, U.S. Appl. No. 13/836,973, filed Mar. 15,
2013. cited by applicant .
[NPL241] US Application, U.S. Appl. No. 14/259,655, filed Apr. 23,
2014. cited by applicant .
[NPL242] US Application, U.S. Appl. No. 14/259,589, filed Apr. 23,
2014. cited by applicant .
[NPL264] PCT/US2014/014357 International Search Report and Written
Opinion dated May 19, 2014. cited by applicant .
[NPL268] Ronco et al. 2008, Cardiorenal Syndrome, Journal American
College Cardiology, 52:1527-1539, Abstract. cited by applicant
.
[NPL26] Overgaard, et. al., Activity-induced recovery of
excitability in K+-depressed rat soleus muscle, Am. J. P 280:
R48-R55, Jan. 1, 2001. cited by applicant .
[NPL27] Overgaard. et. al., Relations between excitability and
contractility in rate soleusmuscle: role of the Na+--K+ pump and
Na+--K--S gradients. Journal of Physiology, 1999, 215-225, 518(1).
cited by applicant .
[NPL285] Zoccali, Pulmonary Congestion Predicts Cardiac Events and
Mortality in ESRD, Clinical Epidemiology, J. Am Soc Nephrol
24:639-646, 2013. cited by applicant .
[NPL306] Coast, et al. 1990, An approach to Cardiac Arrhythmia
analysis Using Hidden Markov Models, IEEE Transactions on
Biomedical Engineering. 1990, 37(9):826-835. cited by applicant
.
[NPL309] Weiner, et. al., Article: Cardiac Function and
Cardiovascular Disease in Chronic Kidney Disease, Book: Primer on
Kidney Diseases (Author: Greenberg, et al), 2009,499-505, 5th Ed.,
Saunders Elsevier, Philadelphia, PA. cited by applicant .
[NPL310] U.S. Appl. No. 61/480,532. cited by applicant .
[NPL311] U.S. Appl. No. 13/424,479. cited by applicant .
[NPL312] U.S. Appl. No. 13/424,429 dated Nov. 1, 2012. cited by
applicant .
[NPL313] U.S. Appl. No. 13/424,525. cited by applicant .
[NPL317] U.S. Appl. No. 61/480,530. cited by applicant .
[NPL318] U.S. Appl. No. 61/480,528 dated Apr. 29, 2011. cited by
applicant .
[NPL322] Velasco, Optimal Fluid Control can Normalize
Cardiovascular Risk Markers and Limit Left Ventricular Hypertrophy
in Thrice Weekly Dialysis Patients, Hemodialysis Intenational,
16:465-472, 2012. cited by applicant .
[NPL323] Whitman, CKD and Sudden Cardiac Death: Epidemiology,
Mechanisms, and Therapeutic Approaches, J Am Soc Nephrol,
23:1929-1939, 2012. cited by applicant .
[NPL324] Hall, Hospitalization for Congestive Heart Failure: United
States, 2000-2010, NCHS Data Brief, No. 108, Oct. 2012. cited by
applicant .
[NPL527] Office Action in U.S. Appl. No. 12/571,127 dated Dec. 17,
2015. cited by applicant .
[NPL539] Office Action in U.S. Appl. No. 12/571,127 dated Nov. 8,
2012. cited by applicant .
[NPL540] Office Action in U.S. Appl. No. 14/554,338 dated Jun. 7,
2016. cited by applicant .
[NPL541] Office Action in U.S. Appl. No. 14/554,338 dated Sep. 28,
2016. cited by applicant .
[NPL542] Office Action in U.S. Appl. No. 14/554,272 dated Aug. 8,
2016. cited by applicant .
[NPL543] Office Action in U.S. Appl. No. 13/424,479 dated Oct. 25,
2014. cited by applicant .
[NPL545] Office Action in U.S. Appl. No. 14/566,686 dated Apr. 28,
2016. cited by applicant .
[NPL547] Office Action in Chinese Application No. 201510511657.9
dated Dec. 28, 2016. cited by applicant .
[NPL55] U.S. Appl. No. 13/424,454. cited by applicant .
[NPL57] U.S. Appl. No. 13/424,467. cited by applicant .
[NPL582] Office Action in U.S. Appl. No. 13/757,792 dated Apr. 6,
2015. cited by applicant .
[NPL62] U.S. Appl. No. 13/424,533. cited by applicant .
[NPL632] Lakerveld et al, Primary prevention of diabetes mellitus
type 2 and cardiovascular diseases using a cognitive behavior
program aimed at lifestyle changes in people at risk: Design of a
randomized controlled trial, 2008, BMC Endocrine Disorders, 8(6):
1-19. cited by applicant .
[NPL633] Gordhandas et al, Real-Time Extraction and Analysis of Key
Morphological Features in the Electrocardiogram, for Data
Compression and Clinical Decision Support, 2004, Computational
Physiology, pp. 15-18. cited by applicant .
[NPL671] European Office Action in Application 12717020.7 dated
Dec. 11, 2015. cited by applicant .
[NPL672] PCT/US2012/034331 International Preliminary Report on
Patentability and Written Opinion dated Oct. 29, 2013. cited by
applicant .
[NPL674] Office Action in Chinese Application No. 201280020932.1
dated Jan. 7, 2015. cited by applicant .
[NPL675] Office Action in Chinese Application No. 201280020932.1
dated Apr. 3, 2015. cited by applicant .
[NPL67] U.S. Appl. No. 13/424,490. cited by applicant .
[NPL68] U.S. Appl. No. 13/424,517. cited by applicant .
[NPL693] PCT/US2012/034330, International Search Report and Written
Opinion dated Aug. 28, 2012. cited by applicant .
[NPL699] Office Action in Chinese Application No. 201280020937.4
dated Oct. 22, 2016. cited by applicant .
[NPL700] Office Action in Japanese Application No. 2014-508434
dated Nov. 16, 2015. cited by applicant .
[NPL701] Office Action in Japanese Application No. 2014-508434
dated Dec. 8, 2014. cited by applicant .
[NPL702] Office Action in Japanese Application No. 2014-508434
dated Nov. 4, 2016. cited by applicant .
[NPL703] Office Action in European Application No. 12717019.9 dated
Feb. 16, 2017. cited by applicant .
[NPL706] Office Action in Chinese Application No. 201510511657.9
dated May 10, 2017. cited by applicant .
[NPL709] PCT/US2014/065201 International Preliminary Report on
Patentability dated May 19, 2016. cited by applicant .
[NPL727] Office Action in European Application No. EP 12717021.5
dated Feb. 3, 2017. cited by applicant .
[NPL735] Office Action in Chinese Application No. 201510593695.3
dated Jul. 12, 2017. cited by applicant .
[NPL748] Office Action in European Application No. EP 12719170.8
dated Jan. 14, 2015. cited by applicant .
[NPL749] Office Action in Japanese Application No. JP 2014-508437
dated Dec. 8, 2014. cited by applicant .
[NPL757] U.S. Appl. No. 60/650,497 dated Feb. 7, 2005. cited by
applicant .
[NPL81] U.S. Appl. No. 61/480,539 dated Apr. 29, 2011. cited by
applicant .
[NPL84] U.S. Appl. No. 61/480,535 dated Apr. 29, 2011. cited by
applicant .
[NPL90] Nedelkov, et. al., Design of buffer exchange surfaces and
sensor chips for biosensor chip mass spectrometry, Proteomics,
2002, 441-446, 2(4). cited by applicant .
[NPL] European Search Report App 14865374.4, dated Jun. 12, 2017.
cited by applicant .
[NPL] European Search Report for Application No. 14865128.4 dated
Jun. 20, 2017. cited by applicant .
[NPL] Green et al., Sudden Cardiac Death in Hemodialysis Patients:
an In-Depth Review , Am J Kidney Dis 57(6)921:929. cited by
applicant .
[NPL] Rajan et al. Generalized Feature Extraction for Time-Varying
Autoregressive Models, IEEE Transacion Signal Processing vol. 44,
No. 10. cited by applicant .
Chinese Office Action in App. No. 201480059332.5, dated Mar. 30,
2018. cited by applicant .
European Search Report for App. No. 14859115.9, dated Jan. 5, 2018.
cited by applicant .
European Search Report for App. No. 17185636.2 dated Jan. 10, 2018.
cited by applicant .
European Search Report for App. No. 17190053.3, dated Jan. 2, 2018.
cited by applicant .
European Search Report for App. No. 17190066, dated Jan. 16, 2018.
cited by applicant .
European Search Report for App. No. 17190084, dated Feb. 9, 2018.
cited by applicant .
Laurent, Jeanpierre, "Continuous Monitoring of Dynamic Systems:
Application to Monitoring of Dialyzed Patients" Oct. 30, 2004,
received from internet:
http://laurent.jeanpierre1.free.fr/recherche/papiers/aista2004.pdf.
cited by applicant .
PCT/US2016/058579 International Search Report dated Jan. 31, 2017.
cited by applicant .
PCT/US2016/058579_WO. cited by applicant .
PCT/US2017/025868 International Search Report dated Jun. 29, 2017.
cited by applicant .
PCT/US2017/025868 Written Opinion dated Jun. 29, 2017. cited by
applicant .
PCT/US2017/030377_ISR. cited by applicant .
PCT/US2017/030377_WO. cited by applicant .
PCTUS2017025858 International Search Report dated Jun. 29, 2017.
cited by applicant .
PCTUS2017025858 Written Opinion dated Jun. 29, 2017. cited by
applicant .
PCTUS2017025876 International Search Report dated Jun. 29, 2017.
cited by applicant .
PCTUS2017025876 Written Opinion dated Jun. 29, 2017. cited by
applicant .
Wollenstein, et al, "Colorimetric gas sensors for the detection of
ammonia, nitrogen dioxide, and carbon monoxide: current status and
research trends", Sensor and Test Conference 2011, Jan. 2, 2011,
pp. 562-567. cited by applicant.
|
Primary Examiner: Deak; Leslie R
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. patent application Ser.
No. 14/539,437 filed Nov. 12, 2014, which claims benefit of and
priority to U.S. Provisional Application No. 61/899,875 filed Nov.
4, 2013, and the disclosures of each of the above-identified
applications are hereby incorporated by reference in their
entirety.
Claims
We claim:
1. A method, comprising the steps of: connecting a patient to a
medical device to obtain input parameters correlated for fluid
removal; transferring the input parameters to a medical device
processor having a shielded circuitry to reduce electric shock to
the patient, wherein the medical device processor is implemented
with a forward algorithm to generate a recommended fluid level
(DFL) based on the input parameters, and wherein the forward
algorithm is presented by the equation:
DFL=W1*D1(P1,X1)+W2*D2(P2,X2)+W3*D2(P3,X3)+W4*D1(P4,X4)+W5*D1(P5,X5)+W6*D-
2(P6,X6)+W7*D2(P7,X7)+W8*D2(P8,X8)+W9*D2(P9,X9)+W10*D2(P10,X10)+W12*D1(P12-
,X12)+W13*D1(P13,X13)+W14*D2(P14,X14)+W15*D1(P15,X15)+W16*D1(P16,X16);
where P1 through P16 are each an input parameter, W1 through W16
are each a weighting coefficient predetermined according to a level
of impact on fluid removal by a corresponding input parameter, X1
through X16 are each a cut-off trigger predetermined when a
corresponding input parameter indicates no recommended change in
fluid level, D1 is a function with an output of either 0 and 1 and
increases from 0 to 1 as the value of the input parameter increases
to a value greater than its corresponding cut-off trigger, D2 is a
function with an output is either 0 or 1 and deceases from 1 to 0
as the value of an input parameter is greater than the value of the
cut-off trigger, where input parameters associated with D1
correlate to an excess of fluid in the patient when increased and
the input parameters associated with D2 correlate to an excess of
fluid in the patient when decreased; and adjusting fluid removal
from the patient based on the recommended fluid level.
2. The method of claim 1, wherein the one or more parameters
positively correlated for fluid removal are selected from the group
consisting of atrial fibrillation burden, respiratory rate, sleep
pattern, dialysis markers, patient weight, patient medications and
dosage, patient supplied data indicative of fluid removal, and
clinical data indicative of fluid removal.
3. The method of claim 1, wherein the one or more parameters
negatively correlated for fluid removal are selected from the group
consisting of tissue impedance, ambulatory heart rate variability,
body temperature, heart rate change during dialysis, heart rate
variability during dialysis, blood pressure reduction during
dialysis, mixed venous oxygen saturation, and patient medications
and dosage indicative of fluid retention.
Description
FIELD OF THE INVENTION
The invention relates to an electronic device that can monitor the
fluid levels in a mammal with heart failure or kidney disease, and
can support decision making regarding the amount of fluid to be
removed from the subject during a hemodialysis session. The systems
and methods of the invention include electronic circuits,
electronic sensors, a computer processor, algorithm(s) and a
telecommunications set-up. The invention further relates to methods
for signal processing and patient monitoring.
BACKGROUND
Functioning kidneys of the mammals remove excess fluids,
electrolytes, and other molecules. In patients with Chronic Kidney
Disease (CKD), kidney function is severely compromised. Chronic
kidney disease (CKD), also known as chronic renal disease, is a
progressive loss in renal function over a period of months or
years. The co-morbidities of CKD are diabetes and high blood
pressure, which are indicated to be responsible for up to
two-thirds of the cases. Heart disease and the associated cardiac
arrhythmias are the leading cause of death for many people
suffering from CKD. Excessive fluid, ions and other toxins
accumulate in patients with CKD. Although these patients are
usually treated by hemodialysis therapy, the treatment is not
continuous, but periodic, causing the build-up of excessive amount
of fluids in the body between hemodialysis sessions.
Fluid buildup in the body is also a concern for patients with heart
failure, a debilitating medical condition in which the ability of
the heart muscle to pump the blood is reduced. As the contractions
of the heart become weaker and the ability of the heart to push the
blood into the arteries is reduced, both the stroke volume and the
cardiac output decrease. The volume of blood remaining in the veins
increase, causing the fluid to build in the tissues of the subject.
The ability of the kidney to excrete excess salts and water is also
reduced during the heart failure, further increasing the overall
fluid accumulation. Excess fluid can build up in various locations
in the body, leading to a general condition known as edema. Edema
may cause the swelling that occurs in the feet, ankles and the
legs, in which case it is called the peripheral edema. It may also
occur in the lungs as with pulmonary edema or in the abdomen as in
ascites.
Fluid accumulation in the body causes multiple problems for the
patient and the health care system. Peripheral edema causes local
pain and discomfort. Pulmonary edema creates breathing difficulties
and makes it difficult for the patients to sleep. Ascites increases
the external pressure on the vena cava, resulting in the reduced
blood return to the heart from the systemic circulation. The
increased blood volume elevates the load on the already weakened
cardiac muscle, making the heart failure even worse. The increased
blood volume also increases the systolic blood pressure which
worsens the kidney failure. The stretching of the thin walls of the
atria due to the excess blood volume increases the incidence of
atrial fibrillation. Given all the negative effects of the volume
overload, fluid overload in the patients, especially for ones with
kidney or heart disease, must be managed carefully. Otherwise,
patients require frequent hospitalizations, which is inconvenient
and costly to medical system.
Over the years, many methods have been developed to treat the fluid
overload in patients suffering from cardiac and kidney failure. The
methods range from pharmaceutical therapies such as the
administration of diuretics to increase urine production by the
kidneys to the physical methods to directly remove fluids such as
ultrafiltration, hemodialysis and peritoneal dialysis. Diuretics
increase the urine production by the kidneys to enhance the removal
of the fluids using the residual function of the renal system.
Although using diuretics reduces the fluid volumes, some patients
do not respond to this therapy, either due to the resistance of
their body to the drug, or due to the complete failure of their
kidneys. Diuretics also contribute to a phenomenon known as the
downward spiral in patients with heart failure as illustrated in
FIG. 1. Briefly, the administration of diuretics 101 initially
leads to the reductions in the fluid volume 102. As the fluid
volume is decreased, the venous pooling of the blood is also
reduced, providing some relief to the heart 103. The filling of the
heart is also reduced 104 due to the lower pressure of blood in the
venous system. Since the heart is not filled completely, cardiac
muscle is not fully stretched; hence it does not contract
vigorously, but instead produces a weak ejection 105 and 106. This
once again causes increased fluid accumulation 107 in the lungs and
in the other tissues, leading to the worsening of the forward heart
failure 108, which is the inability of the heart to deliver
adequate amounts of blood into the arteries. After repeating the
cycle in FIG. 1 many times, diuretics eventually become ineffective
and the symptoms of the patient can no longer be treated with the
administration of additional diuretics. The situation is further
illustrated in FIG. 2 where the venous return 201 and cardiac
output 202 are shown as a function of the right atrial pressure
203. In that diagram, the normal venous return 204 and cardiac
output 205 are shown at point 206. Fluid overload elevates the
right atrial pressure, resulting in the reduction of both the
venous return and the cardiac output of the patient to the point at
207. Chronic stretching of the atrial wall due to the excess fluid
volumes and the increased atrial pressures lead to the onset of
cardiac arrhythmias, usually in the form of atrial
fibrillation.
Hence, there is a need for methods and systems that overcome the
limitations of diuretics. There is also a need for methods and
systems to treat the fluid overload in patients suffering from
cardiac and kidney failure that overcome limitations associated
with pharmaceutical therapies and physical methods to directly
remove fluids such as ultrafiltration, hemodialysis and peritoneal
dialysis.
SUMMARY OF THE INVENTION
The invention is directed to a method for monitoring and treatment
of subjects with cardiac disease or kidney disease receiving
dialysis treatment. Related medical systems, methods for the build
of implantable devices and external monitoring and treatment
devices are provided.
In one embodiment, the method can have the steps of obtaining one
or more parameters from the patient, communicating the parameters
to a medical device processor, wherein the medical device processor
utilizes those parameters via a forward algorithm to generate a
result, and determining the recommended patient fluid level based
on the result.
In another embodiment, the method also can have the step of
changing the fluid level in a patient to the recommended fluid
level.
In one embodiment, the forward algorithm can compute the
recommended change in fluid level in a patient wherein the
recommended patient fluid level is based on one or more parameters
positively correlated for fluid removal and one or more parameters
negatively correlated for fluid removal.
In another embodiment, the one or more parameters positively
correlated for fluid removal and the one or more parameters
negatively correlated for fluid removal can be multiplied by a
weighting coefficient.
In one embodiment, the one or more parameters positively correlated
for fluid removal can be a function of an input parameter, an
off-set coefficient, and a scaling coefficient, and the one or more
parameters negatively correlated for fluid removal can be a
function of an input parameter, an off-set coefficient and a
scaling coefficient.
In another embodiment, the one or more parameters positively
correlated for fluid removal can be selected from the group of
atrial fibrillation burden, respiratory rate, sleep pattern,
dialysis markers, patient weight, patient medications and dosage,
patient supplied data indicative of fluid removal, and clinical
data indicative of fluid removal.
In one embodiment, the one or more parameters negatively correlated
for fluid removal can be selected from the group of tissue
impedance, ambulatory heart rate variability, body temperature,
heart rate change during dialysis, heart rate variability during
dialysis, blood pressure reduction during dialysis, mixed venous
oxygen saturation, and patient medications and dosage indicative of
fluid retention.
In another embodiment, the forward algorithm can be computed from a
function. In one embodiment, one or more of the parameters can be
obtained from the patient prior to dialysis. In another embodiment,
one or more of the parameters can be obtained from measurements
made during dialysis.
In one embodiment, one or more of the parameters can be obtained
from the patient's medical records.
In another embodiment, the parameters can be atrial fibrillation
burden, tissue impedance, heart rate variability, sleep pattern and
body weight.
In one embodiment, the method can also have the step of utilizing a
computer to calculate the recommended fluid level in a patient.
In one embodiment, the method also can have the step of changing
the fluid level in a patient to the recommended amount by
ultrafiltration.
In one embodiment, the weighting coefficients, off-set coefficients
and scaling coefficients can be adjusted during dialysis by using a
backward algorithm and patient results.
In one embodiment, the backward algorithm can utilize data from
multiple sessions of the patient.
In one embodiment, the parameter positively can be correlated for
fluid removal is selected from the group consisting of pulmonary
arterial pressure, venous pressure and atrial pressure.
In one embodiment, the forward algorithm can compute the function
DFL=W1*D1(P1,Xi)+W2*D2(P2,X2)+W3*D2(P3,X3)+W4*D1(P4,X4)+W5*D1(P5,X5)+W6*D-
2(P6,X6)+W7*D2(P7,X7)+W8*D2(P8,X8)+W9*D2(P9,X9)+W10*D2(P10,X10)+W12*D1(P12-
,X12)+W13*D1(P13,X13)+W14*D2(P14,X14)+W15*D1(P15,X15)+W16*D1(P16,X16);
where DFL is the recommended change in fluid level in a patient, W1
through W16 are weighting coefficients, X1 through X16 are cut-off
triggers, P1 through P16 are input parameters, D1 is a function
wherein an output is either 0 and 1 and increases from 0 to 1 as
the value of the input parameter increases to a value greater than
cut-off trigger, D2 is a function wherein an output is either 0 or
1 and the value decreases from 1 to 0 as the value of an input
parameter is greater than the value of the cut-off trigger, wherein
parameters associated with D1 correlate to an excess of fluid in
the patient when increased and the parameters associated with D2
correlate to an excess of fluid in the patient when decreased.
In one embodiment, the parameters used in the method can be
selected from the group comprising atrial fibrillation burden,
tissue impedance, ambulatory heart rate variability, respiratory
rate, sleep pattern, body temperature, heart rate change during
dialysis, heart rate variability during dialysis, blood pressure
reduction during dialysis, mixed venous oxygen saturation, fluid
removed during dialysis session, dialysis markers, patient weight,
patient medications and dosage, patient supplied data, and clinical
data.
The invention can be a system for measuring the recommended change
in patient fluid level. In one embodiment, the system can be made
from one or more sources of parameters from a patient, a medical
device processor in electronic communication with the sources of
the parameters, wherein the medical device processor utilizes a
forward algorithm to calculate a recommended change in patient
fluid level.
In another embodiment, the system can be a computer that calculates
the recommended change in patient fluid level.
In one embodiment, the forward algorithm can compute the
recommended fluid level in a patient wherein the recommended fluid
level is based on one or more parameters positively correlated for
fluid removal and one or more parameters negatively correlated for
fluid level.
In one embodiment, the computer can determine the recommended
change in patient fluid level by a forward algorithm given by the
function
.times..function. ##EQU00001## wherein DFL is the recommended
change in fluid level, P1 through Pn are input parameters, Sx is
one of the group consisting of S1, S2, and S3, S1 is a function
wherein the output is between 0 and 1 and increases as the value of
the input parameter increases, S2 is a function wherein the output
is between 0 and 1 and the value decreases as the value of the
input parameter increases, S3 is a function wherein the output is
between 0 and 1 and the value increases as the value of the input
parameter varies from a set point, Sx is S1 where the parameter is
positively correlated to fluid removal, Sx is S2 where the
parameter is negatively correlated to fluid removal, Sx is S3 where
the parameter correlates to fluid removal as it deviates from a set
point, W1 through Wn are weighting coefficients, C1 through Cn are
off-set coefficients, K1 through Kn are scaling coefficients, and n
is the number of parameters.
In one embodiment, n can be between 5 and 100.
In one embodiment, the one or more parameters positively correlated
for fluid removal can be selected from the group consisting of
atrial fibrillation burden, respiratory rate, sleep pattern,
dialysis markers, patient weight, patient medications and dosage,
patient supplied data indicative of fluid removal and clinical data
indicative of fluid removal.
In one embodiment, the one or more parameters can be positively
correlated for fluid removal includes pulmonary arterial pressure,
venous pressure or atrial pressure.
In one embodiment the one or more parameters negatively correlated
for fluid removal can be selected from the group consisting of
tissue impedance, ambulatory heart rate variability, body
temperature, heart rate change during dialysis, heart rate
variability during dialysis, blood pressure reduction during
dialysis, mixed venous oxygen saturation, and patient medications
and dosage indicative of fluid retention.
In one embodiment, the one or more sources of parameters used in
the system can be measurements from an implantable device.
In one embodiment, the one or more sources of parameters used in
the system can be manually entered data.
In one embodiment, the one or more sources of the parameters used
by the system can be measurements taken from the patient prior to
dialysis.
In another embodiment, the system further can be a signaling
mechanism to signal when there is a recommended change in the
patient's fluid level.
Other objects, features and advantages of the present invention
will become apparent to those skilled in the art from the following
detailed description. The detailed description and specific
examples, while indicating some embodiments of the present
invention are given by way of illustration and not limitation. Many
changes and modifications within the scope of the present invention
may be made without departing from the spirit of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of events describing the downward spiral
in patients with heart failure.
FIG. 2 is a graphical illustration of changes in the pumping
function of the mammalian heart during fluid overload.
FIG. 3 is a block diagram showing the utilization of the sensory
data obtained from the transducers used by the present
invention.
FIG. 4 is a schematic drawing of the invention.
FIG. 5a is a discrete function converting information into scores
for D.sub.1(x).
FIG. 5b is a discrete function converting information into scores
for D.sub.1(x).
FIG. 5c is a discrete function converting information into scores
for D.sub.2(x).
FIG. 6a is a continuous function converting information into scores
for S.sub.1(x).
FIG. 6b is a continuous function converting information into scores
for S.sub.2(x).
FIG. 6c is a continuous function converting information into scores
for S.sub.3(x).
FIG. 7 is a block diagram showing the utilization of the data used
by the present invention.
DETAILED DESCRIPTION OF THE INVENTION
Unless defined otherwise, all technical and scientific terms used
herein generally have the same meaning as commonly understood by
one of ordinary skill in the relevant art.
The articles "a" and "an" are used herein to refer to one or to
more than one (i.e., to at least one) of the grammatical object of
the article. For example, "an element" means one element or more
than one element.
The term "backward algorithm," describes a series of steps or
protocols whether computer implemented or not, that can affect one
parameters and be correlated back to additional individual
parameters.
"Chronic kidney disease" (CKD) is a condition characterized by the
slow loss of kidney function. The most common causes of CKD are
high blood pressure, diabetes, heart disease, and diseases that
cause inflammation in the kidneys. Chronic kidney disease can also
be caused by infections or urinary blockages. If CKD progresses, it
can lead to end-stage renal disease (ESRD), where the kidneys fail
completely.
The terms "communicate" and "communication" include but are not
limited to, the connection between the electrical elements of the
system, either directly or wirelessly, using optical,
electromagnetic, electrical, acoustic or mechanical connections,
for data transmission among and between said elements.
The term "comprising" includes, but is not limited to, whatever
follows the word "comprising." Use of the term indicates the listed
elements are required or mandatory but that other elements are
optional and may or may not be present.
The term "consisting of" includes and is limited to whatever
follows the phrase the phrase "consisting of." The phrase indicates
the limited elements are required or mandatory and that no other
elements may be present.
A "control system" consists of combinations of components that act
together to maintain a system to a desired set of performance
specifications. The performance specifications can include sensors
and monitoring components, processors, memory and computer
components configured to interoperate.
A "controller" or "control unit" is a device which monitors and
affects the operational conditions of a system. The operational
conditions are typically referred to as output variables of the
system, which can be affected by adjusting certain input
variables.
"Dialysis" is a type of filtration, or a process of selective
diffusion through a membrane. Dialysis removes solutes of a
specific range of molecular weights via diffusion through a
membrane from a fluid to be dialyzed into a dialysate. During
dialysis, a fluid to be dialyzed is passed over a filter membrane,
while dialysate is passed over the other side of that membrane.
Dissolved solutes are transported across the filter membrane by
diffusion between the fluids. The dialysate is used to remove
solutes from the fluid to be dialyzed.
An "electrocardiogram" or "ECG" is a time varying waveform,
produced by the electrical activity of the cardiac muscle and the
associated electrical network within the myocardium. Term is used
interchangeably for the ECG tracing available from an external ECG
recording, or from an implantable device recording.
The term "forward algorithm" describes an series of step,
procedures or protocols whether computer implemented or not, used
to convert data from information sources into a value.
"Heart failure" (HF) is a condition characterized by the loss of
the pumping function of cardiac muscle. The most common causes of
HF are coronary artery disease, high blood pressure, diabetes and
obesity. Although it can be managed for extended periods of time,
there is no known cure.
A "medical device processor" refers a special purpose processor
that can have any one of the following functions of controlling the
collection of external or implantable medical device data,
controlling the collection of metadata based on the collected data
of any type, synchronizing data, and combinations thereof.
A "patient" is a member of any animal species, preferably a
mammalian species, optionally a human. The subject can be an
apparently healthy individual, an individual suffering from a
disease, or an individual being treated for an acute condition or a
chronic disease.
The term "parameters positively correlated for fluid removal" are
defined as the parameters shown in Table 1 having a "+" sign in the
third column and signify an additive effect on an equation. The
term "parameters negatively correlated for fluid removal" are
defined as the parameters shown in Table 1 having a "-" sign in the
third column and signify a subtractive effect on an equation.
The term "processor" as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary
skill in the art. The term refers without limitation to a computer
system, state machine, processor, or the like designed to perform
arithmetic or logic operations using logic circuitry that responds
to and processes the basic instructions that drive a computer. In
some embodiments, the terms can include ROM ("read-only memory")
and/or RAM ("random-access memory") associated therewith.
The term "programmable" as used herein refers to a device using
computer hardware architecture and being capable of carrying out a
set of commands, automatically.
The term "sensory unit" refers to an electronic component capable
of measuring a property or condition of interest.
The terms "treating" and "treatment" refer to the management and
care of a patient having a pathology or condition. Treating
includes administering one or more embodiments of the present
invention to prevent or alleviate the symptoms or complications or
to eliminate the disease, condition, or disorder. As used herein,
"treatment" or "therapy" refers to both therapeutic treatment and
prophylactic or preventative measures. "Treating" or "treatment"
does not require complete alleviation of signs or symptoms, does
not require a cure, and includes protocols having only a marginal
or incomplete effect on a patient.
Components of the Invention
The present invention comprises the following components: A set of
information sources including the sensors on implantable or
external devices to monitor the physiological condition of the
patient, a computing unit to process the information, and a
communication system to relay the information between the sensors,
the processing unit, the patient and the medical care
personnel.
Unless specifically stated otherwise, as apparent from the
foregoing discussions, it should be appreciated that throughout the
specification discussions utilizing terms such as "processing,"
"computing," "calculating," "determining," or the like, refer to
the action and/or processes of a computer or computing system, or
similar electronic computing device, that manipulate and/or
transform data represented as physical, such as electronic,
quantities within the computing system's registers and/or memories
into other data similarly represented as physical quantities within
the computing system's memories, registers or other such
information storage, transmission or display devices. In a similar
manner, the term "processor" may refer to any device or portion of
a device that processes electronic data from registers and/or
memory to transform that electronic data into other electronic data
that may be stored in registers and/or memory. A "computing
platform" may comprise one or more processors.
Embodiments of the present invention may include apparatuses and/or
devices for performing the operations herein. An apparatus may be
specially constructed for the desired purposes, or it may comprise
a general purpose device selectively activated or reconfigured by a
program stored in the device. In yet another exemplary embodiment,
the invention may be implemented using a combination of any of,
e.g., but not limited to, hardware, firmware, and software,
etc.
The methods, software and hardware described herein can be embodied
in or use transitory or non-transitory computer readable media with
instructions that cause a programmable processor to carry out the
techniques described herein. A "computer-readable medium" includes
but is not limited to read-only memory, Flash memory, EPROM and a
magnetic or optical storage medium. A non-transitory computer
readable medium includes all computer readable media except for a
transitory, propagating signal.
Information Sources
Sensors used in the present invention provide the sources of data
necessary for the processing unit to interpret. As shown in FIG. 3,
they are grouped in three categories:
Category 1 of the Information Source is the data that may be
obtained from an implantable device 301. This category includes the
atrial fibrillation (AF) burden, tissue impedance, heart rate
variability (HRV), respiration, sleep pattern and body temperature.
Their relevance and utility in fluid management are described
herein. Although these measurements can be obtained by an
implantable medical device, external sensors may alternatively be
utilized to obtain accurate readings.
AF burden increases as the fluid retention increases since extra
fluid in the circulatory system stretches the atria triggering
episodes of atrial fibrillation. Hence, an increase in AF burden
indicates a need for additional fluid removal. AF burden can be
measured by an implantable device monitoring the ECG of the
patient. Alternatively, AF burden can be measured using an external
device to monitor the ECG of the patient. Normally, AF episodes
last less than 5 minutes and occur less than 1% of the time. Longer
or more frequent episodes are indicative of a need for additional
fluid removal as would be recognized by one of ordinary skill in
the art.
Tissue impedance decreases as the fluid retention increases because
extra fluid in the body reduces the tissue impedance. Hence, a
decrease in the tissue impedance indicates a need for additional
fluid removal. Tissue impedance can be measured by an implantable
device monitoring the electrical impedance between two electrodes.
Tissue impedance that is less than the patient's median tissue
impedance measured over time would indicate a need for additional
fluid removal. While changes in salt concentration do lower tissue
impedance, the majority of impedance change is due to changes in
fluid levels.
Heart rate variability decreases as the fluid retention increases
since extra fluid in the body increases the heart failure. Hence, a
decrease in the heart rate variability indicates a need for
additional fluid removal. Heart rate variability can be measured by
an implantable device monitoring the ECG of the subject, or
alternatively an external device that measures the ECG of the
subject. A peak ambulatory HRV of less than 0.15 Hz would be
indicative of a need for additional fluid removal.
Respiratory rate increases as the fluid retention increases since
extra fluid in the lungs reduces the tidal volume. Hence, an
increase in the respiratory rate indicates a need for additional
fluid removal. Respiratory rate can be measured by an implantable
device monitoring the tissue impedance of the subject. A
respiratory rate of more than 15 breaths per minute may be
indicative of a need for additional fluid removal.
Sleep pattern is disturbed as the fluid retention increases since
extra fluid in the lungs reduces the tidal volume and increases the
pulmonary edema. Hence, a disturbance in the sleep pattern
indicates a need for additional fluid removal. Sleep patterns can
be measured by an implantable device monitoring the physical
activity the subject during nocturnal hours. Normally, REM and
non-REM sleep alternates every 90 minutes. Higher frequency of
these changes can be indicative of a need for increased fluid
removal.
The period in which the patient is asleep may be determined in
other ways. In other embodiments, a patient programming device may
allow the patient to signal to the implantable device that the
patient is attempting to sleep. Alternatively, the period during
which the patient is asleep may be determined by using an
accelerometer in the implantable device. When the accelerometer
determines little or no movement for a threshold period of time, it
can determine that the patient is asleep. Another embodiment can
include determining patient sleep patterns by the changes in heart
rate and other known factors indicative of stages of sleep.
Alternatively, the implanted device can determine the patient's
posture, wherein a patient lying down for a threshold period of
time is assumed to be asleep.
Body temperature consistently low can indicate fluid retention.
Hence, a persistent drop in the body temperature can indicate a
need for additional fluid removal. Body temperature can be measured
by an implantable device monitoring the temperature. It can also be
monitored by external thermometer. A body temperature below 36
degrees Celsius may be indicative of a need for additional fluid
removal.
In some embodiments, the implantable medical devices may be
compatible with the processor used to calculate the recommended
change in patient fluid level, and may be set to automatically send
collected data to the processor wirelessly. In other embodiments,
the information may be read by a user, and manually entered into a
computer for processing.
Similarly, where measurements are taken by sensors external to the
patient, the sensors may automatically input the data into the
computer through wired or wireless communication, or the
information can be obtained by the user and manually entered.
Category 2 of the Information Source is data obtained during a
dialysis session 302. This category includes the heart rate, heart
rate variability, blood pressure, mixed venous oxygen saturation,
amount of fluid removed, measured blood markers and body weight.
Their relevance and utility in fluid management are explained
below.
Heart rate is expected to drop during a dialysis session as fluid
is removed from the body. An insufficient drop in heart rate during
a dialysis session may indicate a need for additional fluid
removal. Heart rate can be monitored by an implantable or an
external device, such as an ECG monitor, blood pressure monitor or
a pulse oximeter during a dialysis session. A heart rate drop of
less than five beats per minute may be indicative of a need for
additional fluid removal.
As described herein, the heart rate variability (HRV) is a function
of the fluid levels. As the fluid is removed from the patient
during a dialysis session, one of ordinary skill would expect the
HRV to increase. Therefore, an insufficient increase in HRV during
a dialysis session may indicate a need for additional fluid
removal. HRV can be monitored by an implantable or an external
device, such as an ECG monitor, blood pressure monitor, a pulse
oximeter during a dialysis session, or other known devices
sufficient for the intended purposes known to those of ordinary
skill. Heart rate variability during dialysis that is less than
0.15 Hz may be indicative of a need for additional fluid
removal.
Blood pressure (BP) can be a function of the fluid levels. As the
fluid is removed from the patient during a dialysis session, one of
ordinary skill might expect the BP to decrease. Therefore, an
insufficient decrease in BP during a dialysis session may indicate
a need for additional fluid removal. BP can be monitored by an
implantable or an external device during a dialysis session. A
decrease in blood pressure of less than 10 mm Hg may be indicative
of a need for additional fluid removal.
Another measurement of vascular pressure is the pulmonary arterial,
venous (central or peripheral) or atrial pressure obtained from an
implanted sensor(s) or derived from a non-implanted external sensor
system. An increase in pulmonary artery pressure (diastolic,
systolic or mean); central venous pressure or atrial pressure is
usually a strong indicator of elevated vascular volume following
excess fluid build-up.
A decrease in the mixed venous oxygen saturation may be due to
inadequate oxygen delivery, caused by depressed cardiac output
resulting from decreased preload, abnormal afterload and cardiac
arrhythmias. It could also be due to increased end organ oxygen
extraction due to higher metabolic demand, such as sepsis, fever,
increased work of breathing and agitation. Hence, a decrease in
mixed venous oxygen saturation, absent fever and difficulty of
breathing may indicate a need for additional fluid removal. Mixed
venous oxygen saturation can be measured by the dialysis system
using the blood going into the dialysis system. A mixed venous
oxygen saturation of below 68 mm Hg may be indicative of a need for
additional fluid removal. Whether or not the patient has a fever
may be determined by measuring body temperature through an
implantable medical device or external thermometer.
Fluid removed during a dialysis session can be usually
predetermined, but may be changed during the session if the patient
experiences a hypotensive episode. Hence, the total fluid removed
is a parameter to be recorded during the dialysis so it can be
adjusted based on the output of the fluid management algorithm 310.
This parameter may be automatically entered by the dialysis system
into the computer, or alternatively, the user may determine the
amount of fluid removed and manually enter it into the
computer.
Both the fluid overload and the changes in serum creatinine can be
independently correlated with the mortality of the patients. Hence,
any increase in creatinine would increase the chances of mortality
for the patient, and one can reduce the fluid volumes to compensate
for the increased chance of mortality. Therefore, an increase in
creatinine may indicate a need for additional fluid removal.
Creatinine levels can be obtained from the blood analysis done
periodically, such as once a month.
A rapid increase in patient weight can indicate increased fluid
retention. Hence a sudden raise in the body weight measurement
might necessitate an increase in fluid removal. Weight measurements
can be done during dialysis sessions. A change in patient weight of
more than 2 kilograms may be indicative of a need for excess fluid
removal.
Much of the category 2 information may be obtained from an
implantable medical device. In other embodiments, the information
may be obtained from external sensors and measurements. In some
embodiments, the external sensors can be directly attached to the
dialysis apparatus, thus facilitating easy collection of
information. In other embodiments, external sensors may be in
electronic communication with a computer to automatically add
information to the algorithm calculating the recommended change in
patient fluid level. In other embodiments, the data obtained from
these sensors may be manually added into the computer.
Category 3 of the Information Source 303 can be data entered
manually or transferred electronically from other medical
information sources, such as an electronic medical record (EMR)
system. This category includes the medications taken by the
patient, information supplied by the patient and a set of clinical
data. Their relevance and utility in fluid management are described
herein.
Medications taken can usually change the ability of the patient to
excrete urine, hence alters the fluid retention. For example, a
patient who can no longer tolerate diuretics will often have their
doses of the diuretic reduced, leading to increases in the fluid
retention. In such a situation, the fluid removal rate must be
increased. Medications taken and their dosage can be manually
entered or transferred from the EMR.
Patients themselves can provide information on their health
conditions. For example, they may report they are having difficulty
in sleeping or running out of breath when climbing stairs,
indicating an increase in the fluid retention. In such a situation,
the fluid removal rate must be increased. Patient supplied
information can be manually entered either by the patient or the
medical care personnel.
Clinical information regarding the patient can provide insights
into the overall health of the subject. Recent hospitalizations due
to pulmonary insufficiency could be due to fluid retention. Blood
markers measured at the clinic such BNP can assess the condition of
the heart failure, where an increase in BNP values would warrant
the removal of additional fluids. Clinical information can be
entered manually or transferred from the EMR.
Table 1 provides a summary of the variables, also called
"parameters," as defined herein, their sources and correlations
between the parameter and the changes recommended to the amount of
fluid to be removed from the patient.
The input parameters including information from category 1,
category 2, and category 3, may be communicated to a processor 304
and are shown by 306, 307 and 308 respectively. The processor 304
can operate the algorithm described below 309 to determine a
recommended change in fluid level 310, which is then communicated
to the system or the user 305.
One embodiment of the system used for determining a recommended
change in patient fluid level is shown in FIG. 4. Implantable
medical device 401 may be a unit with no leads or may contain leads
and external sensors. Units with no leads, such as the Medtronic
Reveal device, may have electrodes for sensing electrograms and
tissue impedance or for stimulating. Units with leads, such as
pacemakers, cardiac resynchronization devices and defibrillators,
utilize their leads for sensing electrograms. Implantable medical
device may also have other sensors, such as an internal or external
accelerometer, temperature sensor, and external pressure sensor,
which are external to the device, yet still inside the patient 402.
Implantable device may contain a power source such as a battery, a
computing hardware, or a data storage unit such as electronic
memory and communication hardware. Implantable medical device 401
provides the information in category 1 or category 2. Category 1 or
category 2 information from the implantable device 401 can be
received via telemetry by a receiver unit 403 and conveyed to the
computing unit 404. Alternatively, the information may be read by
the user with an external receiving unit, who will manually enter
the data into the computer.
External sensors 405, such as the blood pressure sensor, are placed
on the patient 402 for the duration of the dialysis session.
Dialysis can be provided with a hemodialysis system 406 connected
to the patient 402 via blood lines 407 and 408. Hemodialysis system
406 can be in communication with a computing unit 404, such as a
computer. External sensors 405 may also provide the information in
category 1 or category 2. The information from the external sensors
405 can be collected by the receiver unit 403 and conveyed to the
computing unit 404. Alternatively, the user may directly obtain the
information collected by the external sensors and manually enter
the information into the computing unit.
Category 3 information may be entered manually by a human 409 or
retrieved from the Electronic Medical Records.
Examples of sources of information on the parameters are shown in
Table 1. One skilled in the art will understand that these are
merely examples of the sources of information and that the data may
be obtained from other devices and other sources.
Computing Unit and Algorithm
The processing of the information collected in all three categories
is performed by the computing unit. The computing unit can be a
specially adapted unit in order to carry out the purposes and steps
described herein. In any embodiment, the sensors described herein
can operate in combination or conjunction with circuitry specially
adapted to the purposes or steps described herein, or in
combination or conjunction with more than one such processor, or in
combination or conjunction with one or more elements of each type,
such as for distinct steps or portions thereof. The computing unit
and the sensors which detect the data in each of the categories are
specifically adapted computers and processors configured or a
medical or healthcare setting. The computers or processors can have
shielded circuitry to prevent electric shock to a patient or
operator. In any embodiment, the computers and processors of the
present invention are not general purpose computers and can have
regulatory approval for approved medical use on patients.
An algorithm called the "forward algorithm" is used to convert the
data from the information sources into a fluid removal indicator,
which will be described herein by Example 1.
Example 1
Initially, a set of scores can be calculated from the sensory
information, using one of the six functions listed in Table 2. A
graphical representation of the same six functions is shown in
FIGS. 5 and 6. Functions D1, D2 and D3 are discrete functions,
which give discrete outcomes of zero or one, whereas, the functions
S1, S2 and S3 are continuous functions with the possibility of
giving any outcomes in the range from zero to one, such as 0.34.
Functions D1, D2 and D3 have the advantage of being easier to
implement because the only requirement is a comparison of the
argument x to a threshold value of x.sub.C, hence the functions D1,
D2, and D3 are easier to implement in a computer. However, the
discrete functions D1, D2, and D3 provide no grey scale information
or proportional response to a given input. Continuous functions S1,
S2 and S3 provide a much more graded response, but impose a heavier
computational burden on the computer by either requiring a
mathematical computation of the equation provided in Table 2 or
requiring the use of a look-up table.
Functions D1 and S1, shown graphically in FIGS. 5a and 6a
respectively, are designed to indicate that the amount of fluid
removed should be increased when the value of the parameter
increases, hence they are suitable for use with the parameters that
have "positive correlations" to the fluid removal, which are the
ones with "+" signs in the third column of Table 1. Opposite is
true for the score functions D2 and S2, shown graphically in FIGS.
5b and 6b respectively, where amount of fluid removed should be
decreased when the value of the parameter increases, hence they are
suitable for use with the parameters that have negative
correlations to the fluid removal, which are the ones with "-"
signs in the third column of Table 1. Functions D3 and C3, shown
graphically in FIGS. 5c and 6c respectively, produce high scores
when the feature deviates from a central value, either by an
increasing or by a decreasing deviation. They are provided for
cases when parameter is supposed to be maintained within a range,
such the serum potassium level.
Below is an example illustrating using the parameters and their
conversion into raw scores. In this example, features P1 through
P16 are as they were described in Table 1.
DFL=W1*S1(P1,C1,K1)+W2*S2(P2,C2,K2)+W3*S2(P3,C3,K3)+W4*S1(P4,C4,K4)+W5*S1-
(P5,C5,K5)+W6*S2(P6,C6,K6)+W7*S2(P7,C7,K7)+W8*S2(P8,C8,K8)+W9*S2(P9,C9,K9)-
+W10*S2(P10,C10,K10)+W12*S1(P12,C12,K12)+W13*S1(P13,C13,K13)+W14*S2(P14,C1-
4,K14)+W15*S1(P15,C15,K15)+W16*S1(P16,C16,K16)
where the DLF is the recommended change for fluid removal; W1, W2,
. . . , W16 are the weighting coefficients; S1 and S2 are the
functions are as defined in Table 2; P1, P2, . . . , P16 are as
defined in Table 1; C1, C2, . . . , C16 are off-set coefficients,
and K1, K2, . . . , K16 are scaling coefficients.
In certain embodiments, the above computation can be called one
form of a forward algorithm using information from Categories 1, 2
and 3, and producing the recommended change in the fluid removal.
The offset coefficients can be determined and set so that the
coefficients will equal the measured parameter when the parameter
indicates no recommended change in fluid level. For example, if a
patient's respiratory rate is 17 bpm in the absence of an excess of
fluid, then the offset coefficient may be set at 17 bpm. The
weighting coefficients can be set to give more weight to the
parameters that are more indicative of a need to change the
patient's fluid level, and to convert the numerical result of the
function into a recommended change in patient fluid level in
volume. If it is found in a particular patient that a particular
parameter is not changing with a reduction in fluid level, and is
found not to correlate to the patient outcome, then the weighting
coefficient can be reduced to 0. This would eliminate that
parameter from the algorithm. The scaling coefficients are set to
determine the slope of the function. If a small change in the input
parameter is highly indicative of a need to change patient fluid
level, the slope of the function should be steep. If slight
deviations in the input parameter are not highly indicative of a
need to change patient fluid level, the slope of the function
should be shallow.
For the calculation of the recommended change, weighting
coefficients and the off-set and scaling coefficients can be
determined. The constants as described herein are collectively
denoted with the symbol M. These constants can be predetermined and
adjusted by medical professionals attending the patient.
Alternatively, the computing unit may adjust these constants based
on the patient outcomes, using a backward algorithm. By utilizing
the backward algorithm, the effect of changing patient fluid level
can be correlated back to the individual parameters. From this, the
proper weighting and scaling coefficients may be determined.
Table 3 provides nominal values for each of the 13 measured
parameters listed. These values may be set as the offset
coefficients in the initial determination of the constants M.
Deviations from these values may be indicative of a need to change
the patient's fluid level. In one embodiments, a computer can
calculate the constants using the backward algorithm wherein the
constants may be updated and/or changed. Additionally, the
patient's medical history may show reasons for other than excess
fluid that can cause a deviation from these values. When initially
setting the constants for the patient's initial dialysis, the
offset coefficients may be changed to reflect the deviations.
In certain embodiments, a computing unit can work to identify the
constants M using the backward algorithm wherein the operation if
the unit in the overall system is shown in FIG. 7. The information
set 501 is fed into the forward algorithm 502 to produce the
recommended change in the fluid removal 503. This value can be
added to the fluid removed during the past dialysis session, 504,
or P11 in Table 1, to produce the desired ultrafiltrate, UF, value
505, which is sent to the hemodialysis clinic 506. Clinical
condition of the patient 507, or P16 in Table 1, can be monitored
over time, and as required the coefficients M 509 are adjusted by
the backward algorithm 508. The backward algorithm can be
constructed using the many known statistical and signal processing
methods, such as the least squares and steepest descent
methodology. The backward algorithm may use data from more than one
patient's dialysis session to modify the coefficient set M where
there is a general correlation. Additionally, the backward
algorithm can use the data from more than one dialysis session for
the same patient.
In certain embodiments, for the initial use by each patient, the
weighting, off-set and scaling coefficients can be derived and
added to the system. These coefficients can be estimated by the
user, or they may be based upon values that are found suitable for
similar patients. Once dialysis begins, the forward algorithm will
determine a recommended change in patient fluid level. At the same
time, the patient outcome can be monitored using the backward
algorithm, which will make changes to the coefficients.
The technical benefit of the adjustments to the coefficients by the
backward algorithm and the adjustments to the recommended fluid
level of the patient is a dynamic process that cannot be
accomplished with pen and paper. The changes to the coefficients
and therefore the changes to the recommended fluid level occur
constantly in order to continuously update the recommended fluid
level. These changes occur too quickly for the calculations to be
performed with the use of pen and paper.
The processors described herein can be medical device processors.
Medical device processors can control the collection of external or
implantable medical device data, control the collection of metadata
based on the collected data, and synchronize the data on a
timeline. The computing unit and the sensors which detect the data
in each of the categories are specific purpose computers and
processors configured or a medical or healthcare setting. The
computers or processors can have shielded circuitry to prevent
electric shock to a patient or operator. In any embodiment, the
computers and processors of the present invention are not general
purpose computers and can have regulatory approval for approved
medical use on patients. The processors also have communication
systems, hardware and software that protect patient privacy by
protecting the information obtained from the patient.
The systems described herein can also obtain historical data from
electronic medical records or other sources. The hardware
configurations of the system allow for transmission of the data
obtained to the patient's electronic medical records, or to a
hospital data hub, handheld device, or monitor. The computers or
processors described herein are specially adapted to receive
patient data from the sensors and immediately perform the necessary
calculations to determine a new recommended fluid level.
One of ordinary skill in the art will realize that not all sixteen
parameters are necessary in order to obtain a recommended change in
patient fluid level. Each of the parameters individually tend to
show whether a change in fluid level is necessary and by how much.
Therefore, an accurate measurement of the recommended change in
patient fluid level may be achieved using significantly less than
all of the parameters. Additionally, other parameters may be found
that also tend to show a need to change the patient's fluid level.
One of ordinary skill in the art will realize that additional
parameters may be utilized without exceeding the scope of the
invention.
The recommended change in fluid level when the number of parameters
used is not 16 would be given by the equation:
.times..function. ##EQU00002##
Where DFL is the recommended change in fluid level; W1 . . . Wn are
the weighting coefficients; P1. Pn are the parameters as defined in
Table 1; C1 . . . Cn are off-set coefficients; K1 . . . Kn are
scaling coefficients; and Sx is the appropriate function as defined
in Table 2 for the given parameter Px. If the value of the given
parameter is positively correlated to a need to change the patient
fluid level, function S1 would be used. If the value of a given
parameter is negatively correlated with a need to change patient
fluid level, then S2 is the appropriate function. If deviations in
the parameter in either direction from some off-set point indicate
a need to change patient fluid level, then S3 is the appropriate
function. For example, a system can be set up where the parameters
utilized are AF burden, tissue impedance, heart rate variability,
sleep pattern and body weight. The off-set coefficients would be
set in the ranges shown in Table 3. Initially, the weighting
coefficients and scaling coefficients would need to be estimated,
but as the patient undergoes treatment, the backward algorithm will
adjust these coefficients. Because there is no gray scale when
using the discrete functions D1, D2 and D3, the only variable is
the cut-off trigger Xc. For using the discrete functions D1, D2,
and D3, the off-set coefficients should be chosen at some point
beyond the normal value. This allows slight variations in the
measured parameters before the function switches from giving a
value of 0 to giving a value of 1. For example, if the patient's
respiratory rate is 13 bpm in the absence of fluid accumulation,
then the cut-off trigger could be set at 17 bpm, which allows some
variation in respiratory rate without changing the result of the
function.
In some embodiments of the invention, a communication system can be
used. The communication system allows transferring data, including
the information in the category 3 information source, recommended
ultrafiltration amounts, and the coefficients M.
It will be apparent to one skilled in the art that various
combinations and/or modifications and variations can be made in the
dialysis system depending upon the specific needs for operation.
Moreover, features illustrated or described as being part of one
embodiment may be used on another embodiment to yield a still
further embodiment.
TABLE-US-00001 TABLE 1 Parameter correlation Parameter Parameter
Parameter Example to fluid Number Name Source Source removal P01 AF
Burden Implantable/ Implantable monitor LINQ + wearable device
manufactured by Medtronic P02 Tissue Impedance Implantable/
Implantable monitor LINQ - wearable device manufactured by
Medtronic P03 Ambulatory HRV Implantable/ Implantable monitor LINQ
- wearable device manufactured by Medtronic P04 Respiratory Rate
Implantable/ Derived from P02 using a + wearable device real time
algorithm P05 Sleep pattern Implantable/ Implantable monitor LINQ +
(nocturnal wearable device manufactured by Medtronic activity) or
Zeo, Fitbit and Lark. P06 Body temperature Implantable/ LINQ or any
other electronic - wearable device thermometer. P07 Heart rate
change Dialysis System/ Derived from P03 or any - during dialysis
Implant ECG monitor P08 HRV during the Dialysis System/ Derived
from P03 or any - dialysis session Implant ECG monitor P09 BP
reduction Dialysis System BP meter with connectivity, - during
dialysis e.g. Omron 10+ or Medron. P10 Mixed venous Dialysis System
Edwards Swan-Ganz - oxygen saturation Oximeter P11 Fluid removed
Dialysis System Fresenius 2008k None during dialysis session P12
Dialysis markers Dialysis Clinic Nova Model 16 Electrolyte +
measured Analyzer periodically P13 Patient weight Dialysis Clinic
LifeSpan DS 1000i Digital + measured Scale P14 Medications and
Manual entry or AthenaHealth EHR - their dosage EMR transfer P15
Patient supplied Manual MyMedical App for iPhone + data entry
(discomfort) P16 Clinical data Manual entry or AthenaHealth her +
EMR transfer
TABLE-US-00002 TABLE 2 Name Mathematical Expression D1
.function.>.ltoreq. ##EQU00003## D2 .function.>.ltoreq.
##EQU00004## D3 .function.>.ltoreq. ##EQU00005## S1
.function..function. ##EQU00006## S2 .function..function.
##EQU00007## S3 .function..function..times..function.
##EQU00008##
TABLE-US-00003 TABLE 3 Parameter Parameter Nominal Number Name
Value Reference P01 AF Burden Episodes < 5 min
http://crm.cardiosource.org/Learn- AF < 1% of time
from-the-Experts/2013/09/DeviceDetected-AFib-
and-Stroke-Risk.aspx?print=1 P02 Tissue Impedance Median value
Internal data measured 10 wks after implant. P03 Ambulatory HRV
Peak occurs in the 0.15-0.40 Hz range P04 Respiratory Rate 10-15
breaths/min P05 Sleep pattern REM - nonREM
http://learn.chm.msu.edu/ (nocturnal activity) alternating 90 min
neuroed/neurobiology_disease/ content/otheresources/
sleepdisorders.pdf P06 Body temperature 36-38 Celsius P07 Heart
rate change 5 bpm during dialysis P08 HRV during the Peak occurs in
the dialysis session 0.15-0.40 Hz range P09 BP reduction during 10
mm Hg dialysis P10 Mixed venous oxygen 69 +/- 1 mm Hg saturation
P11 Fluid removed during 1.5 Liters dialysis session P12 Dialysis
markers K = 3-5 mM measured periodically P13 Patient weight Change
<2 kg measured P14 Medications and their dosage P15 Patient
supplied data (discomfort) P16 Clinical data
* * * * *
References